Trading Off Precision and Recall

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

EJ

Very well structured and delivered course. Progressive introduction of concepts and intuitive description by Andrew really give a sense of understanding even for the more complex area of the training.

OK

Apr 18, 2018

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You need to know, what do you want to get out of this course. It gives you a lot of information, but be prepared to work hard with linear algeabra and make efforts to compute things in Mathlab/Octave.

수업에서

Machine Learning System Design

To optimize a machine learning algorithm, you’ll need to first understand where the biggest improvements can be made. In this module, we discuss how to understand the performance of a machine learning system with multiple parts, and also how to deal with skewed data.

강사:

Andrew Ng

스크립트

In the last video, we talked about precision and recall as an evaluation metric for classification problems with skewed constants. For many applications, we'll want to somehow control the trade-off between precision and recall. Let me tell you how to do that and also show you some even more effective ways to use precision and recall as an evaluation metric for learning algorithms. As a reminder, here are the definitions of precision and recall from the previous video. Let's continue our cancer classification example, where y equals 1 if the patient has cancer and y equals 0 otherwise. And let's say we're trained in logistic regression classifier which outputs probability between 0 and 1. So, as usual, we're going to predict 1, y equals 1, if h(x) is greater or equal to 0.5. And predict 0 if the hypothesis outputs a value less than 0.5. And this classifier may give us some value for precision and some value for recall. But now, suppose we want to predict that the patient has cancer only if we're very confident that they really do. Because if you go to a patient and you tell them that they have cancer, it's going to give them a huge shock. What we give is a seriously bad news, and they may end up going through a pretty painful treatment process and so on. And so maybe we want to tell someone that we think they have cancer only if they are very confident. One way to do this would be to modify the algorithm, so that instead of setting this threshold at 0.5, we might instead say that we will predict that y is equal to 1 only if h(x) is greater or equal to 0.7. So this is like saying, we'll tell someone they have cancer only if we think there's a greater than or equal to, 70% chance that they have cancer. And, if you do this, then you're predicting someone has cancer only when you're more confident and so you end up with a classifier that has higher precision. Because all of the patients that you're going to and saying, we think you have cancer, although those patients are now ones that you're pretty confident actually have cancer. And so a higher fraction of the patients that you predict have cancer will actually turn out to have cancer because making those predictions only if we're pretty confident. But in contrast this classifier will have lower recall because now we're going to make predictions, we're going to predict y = 1 on a smaller number of patients. Now, can even take this further. Instead of setting the threshold at 0.7, we can set this at 0.9. Now we'll predict y=1 only if we are more than 90% certain that the patient has cancer. And so, a large fraction of those patients will turn out to have cancer. And so this would be a higher precision classifier will have lower recall because we want to correctly detect that those patients have cancer. Now consider a different example. Suppose we want to avoid missing too many actual cases of cancer, so we want to avoid false negatives. In particular, if a patient actually has cancer, but we fail to tell them that they have cancer then that can be really bad. Because if we tell a patient that they don't have cancer, then they're not going to go for treatment. And if it turns out that they have cancer, but we fail to tell them they have cancer, well, they may not get treated at all. And so that would be a really bad outcome because they die because we told them that they don't have cancer. They fail to get treated, but it turns out they actually have cancer. So, suppose that, when in doubt, we want to predict that y=1. So, when in doubt, we want to predict that they have cancer so that at least they look further into it, and these can get treated in case they do turn out to have cancer. In this case, rather than setting higher probability threshold, we might instead take this value and instead set it to a lower value. So maybe 0.3 like so, right? And by doing so, we're saying that, you know what, if we think there's more than a 30% chance that they have cancer we better be more conservative and tell them that they may have cancer so that they can seek treatment if necessary. And in this case what we would have is going to be a higher recall classifier, because we're going to be correctly flagging a higher fraction of all of the patients that actually do have cancer. But we're going to end up with lower precision because a higher fraction of the patients that we said have cancer, a high fraction of them will turn out not to have cancer after all. And by the way, just as a sider, when I talk about this to other students, I've been told before, it's pretty amazing, some of my students say, is how I can tell the story both ways. Why we might want to have higher precision or higher recall and the story actually seems to work both ways. But I hope the details of the algorithm is true and the more general principle is depending on where you want, whether you want higher precision- lower recall, or higher recall- lower precision. You can end up predicting y=1 when h(x) is greater than some threshold. And so in general, for most classifiers there is going to be a trade off between precision and recall, and as you vary the value of this threshold that we join here, you can actually plot out some curve that trades off precision and recall. Where a value up here, this would correspond to a very high value of the threshold, maybe threshold equals 0.99. So that's saying, predict y=1 only if we're more than 99% confident, at least 99% probability this one. So that would be a high precision, relatively low recall. Where as the point down here, will correspond to a value of the threshold that's much lower, maybe equal 0.01, meaning, when in doubt at all, predict y=1, and if you do that, you end up with a much lower precision, higher recall classifier. And as you vary the threshold, if you want you can actually trace of a curve for your classifier to see the range of different values you can get for precision recall. And by the way, the precision-recall curve can look like many different shapes. Sometimes it will look like this, sometimes it will look like that. Now there are many different possible shapes for the precision-recall curve, depending on the details of the classifier. So, this raises another interesting question which is, is there a way to choose this threshold automatically? Or more generally, if we have a few different algorithms or a few different ideas for algorithms, how do we compare different precision recall numbers? Concretely, suppose we have three different learning algorithms. So actually, maybe these are three different learning algorithms, maybe these are the same algorithm but just with different values for the threshold. How do we decide which of these algorithms is best? One of the things we talked about earlier is the importance of a single real number evaluation metric. And that is the idea of having a number that just tells you how well is your classifier doing. But by switching to the precision recall metric we've actually lost that. We now have two real numbers. And so we often, we end up face the situations like if we trying to compare Algorithm 1 and Algorithm 2, we end up asking ourselves, is the precision of 0.5 and a recall of 0.4, was that better or worse than a precision of 0.7 and recall of 0.1? And, if every time you try out a new algorithm you end up having to sit around and think, well, maybe 0.5/0.4 is better than 0.7/0.1, or maybe not, I don't know. If you end up having to sit around and think and make these decisions, that really slows down your decision making process for what changes are useful to incorporate into your algorithm. Whereas in contrast, if we have a single real number evaluation metric like a number that just tells us is algorithm 1 or is algorithm 2 better, then that helps us to much more quickly decide which algorithm to go with. It helps us as well to much more quickly evaluate different changes that we may be contemplating for an algorithm. So how can we get a single real number evaluation metric? One natural thing that you might try is to look at the average precision and recall. So, using P and R to denote precision and recall, what you could do is just compute the average and look at what classifier has the highest average value. But this turns out not to be such a good solution, because similar to the example we had earlier it turns out that if we have a classifier that predicts y=1 all the time, then if you do that you can get a very high recall, but you end up with a very low value of precision. Conversely, if you have a classifier that predicts y equals zero, almost all the time, that is that it predicts y=1 very sparingly, this corresponds to setting a very high threshold using the notation of the previous y. Then you can actually end up with a very high precision with a very low recall. So, the two extremes of either a very high threshold or a very low threshold, neither of that will give a particularly good classifier. And the way we recognize that is by seeing that we end up with a very low precision or a very low recall. And if you just take the average of (P+R)/2 from this example, the average is actually highest for Algorithm 3, even though you can get that sort of performance by predicting y=1 all the time and that's just not a very good classifier, right? You predict y=1 all the time, just normal useful classifier, but all it does is prints out y=1. And so Algorithm 1 or Algorithm 2 would be more useful than Algorithm 3. But in this example, Algorithm 3 has a higher average value of precision recall than Algorithms 1 and 2. So we usually think of this average of precision and recall as not a particularly good way to evaluate our learning algorithm. In contrast, there's a different way for combining precision and recall. This is called the F Score and it uses that formula. And so in this example, here are the F Scores. And so we would tell from these F Scores, it looks like Algorithm 1 has the highest F Score, Algorithm 2 has the second highest, and Algorithm 3 has the lowest. And so, if we go by the F Score we would pick probably Algorithm 1 over the others. The F Score, which is also called the F1 Score, is usually written F1 Score that I have here, but often people will just say F Score, either term is used. Is a little bit like taking the average of precision and recall, but it gives the lower value of precision and recall, whichever it is, it gives it a higher weight. And so, you see in the numerator here that the F Score takes a product of precision and recall. And so if either precision is 0 or recall is equal to 0, the F Score will be equal to 0. So in that sense, it kind of combines precision and recall, but for the F Score to be large, both precision and recall have to be pretty large. I should say that there are many different possible formulas for combing precision and recall. This F Score formula is really maybe a, just one out of a much larger number of possibilities, but historically or traditionally this is what people in Machine Learning seem to use. And the term F Score, it doesn't really mean anything, so don't worry about why it's called F Score or F1 Score. But this usually gives you the effect that you want because if either a precision is zero or recall is zero, this gives you a very low F Score, and so to have a high F Score, you kind of need a precision or recall to be one. And concretely, if P=0 or R=0, then this gives you that the F Score = 0. Whereas a perfect F Score, so if precision equals one and recall equals 1, that will give you an F Score, that's equal to 1 times 1 over 2 times 2, so the F Score will be equal to 1, if you have perfect precision and perfect recall. And intermediate values between 0 and 1, this usually gives a reasonable rank ordering of different classifiers. So in this video, we talked about the notion of trading off between precision and recall, and how we can vary the threshold that we use to decide whether to predict y=1 or y=0. So it's the threshold that says, do we need to be at least 70% confident or 90% confident, or whatever before we predict y=1. And by varying the threshold, you can control a trade off between precision and recall. We also talked about the F Score, which takes precision and recall, and again, gives you a single real number evaluation metric. And of course, if your goal is to automatically set that threshold to decide what's really y=1 and y=0, one pretty reasonable way to do that would also be to try a range of different values of thresholds. So you try a range of values of thresholds and evaluate these different thresholds on, say, your cross-validation set and then to pick whatever value of threshold gives you the highest F Score on your crossvalidation [INAUDIBLE]. And that be a pretty reasonable way to automatically choose the threshold for your classifier as well.